Probability-Based Multi-objective Optimization for Material Selection

Probability-Based Multi-objective Optimization for Material Selection
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Artikel-Nr:
9789819939398
Veröffentl:
2023
Einband:
eBook
Seiten:
202
Autor:
Maosheng Zheng
eBook Typ:
PDF
eBook Format:
Reflowable eBook
Kopierschutz:
Digital Watermark [Social-DRM]
Sprache:
Englisch
Beschreibung:

The second edition of this book illuminates the fundamental principle and applications of probability-based multi-objective optimization for material selection in viewpoint of system theory, in which a brand new concept of preferable probability and its assessment as well as other treatments are introduced by authors for the first time. Hybrids of the new approach with experimental design methodologies (response surface methodology, orthogonal experimental design, and uniform experimental design) are all performed; robustness assessment and performance utility with desirable value are included; discretization treatment in the evaluation is presented; fuzzy-based approach and cluster analysis are involved; applications in portfolio investment and shortest path problem are concerned as well. The authors wish this work will cast a brick to attract jade and would make its contributions to relevant fields as a paving stone. It is designed to be used as a textbook for postgraduate and advanced undergraduate students in relevant majors, while also serving as a valuable reference book for scientists and engineers involved in related fields. 

The second edition of this book illuminates the fundamental principle and applications of probability-based multi-objective optimization for material selection in viewpoint of system theory, in which a brand new concept of preferable probability and its assessment as well as other treatments are introduced by authors for the first time. Hybrids of the new approach with experimental design methodologies (response surface methodology, orthogonal experimental design, and uniform experimental design) are all performed; robustness assessment and performance utility with desirable value are included; discretization treatment in the evaluation is presented; fuzzy-based approach and cluster analysis are involved; applications in portfolio investment and shortest path problem are concerned as well. 

The authors wish this work will cast a brick to attract jade and would make its contributions to relevant fields as a paving stone. It is designed to be used as a textbook for postgraduate and advanced undergraduate students in relevant majors, while also serving as a valuable reference book for scientists and engineers involved in related fields. 


Chapter 1 Introduction

It introduces the general idea of multiple objective optimization in material selection, the status of the applications of multiple objective optimization in material selection, the previous methods and their shortcomings, etc.


Chapter 2 Probability-Based Multi-Objective Optimization and Applications

It describes the general idea of probability-based multiple objective optimization, the basis of theoretic meaning in probability theory, the algorithm, and the features, etc. Applications of materials selection, and some other applications in more broader and general issues are given.


Chapter 3 Extension in Condition of the Utility with Interval Number

It describes the extension of probability-based multiple objective optimization in condition of the utility with interval number, some applications of materials selection are given.


Chapter 4 Extension in Condition of the Utility with Desirable Value

It describes the extension of probability-based multiple objective optimization in condition of the utility with desirable value, some applications of materials selection are given.


Chapter 5 Combination of Probability-Based Multi-Objective Optimization with Test Design Methods

It describes the combination of probability-based multi-objective optimization with test design methods, such as orthogonal test design and uniform experimental design. Some applications of materials selection are given.


Chapter 6 Application of Regression Analysis in the Probability-Based Multi-Objective Optimization

Regression analysis is applied to the probability-based multi-objective optimization, it provides a more accurate prediction, especially for test design methods. Some applications of materials selection are given.


Chapter 7 Concluding Remarks

Conclusion of this book and reviewing the up-to-date progress.

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